Overview

Dataset statistics

Number of variables41
Number of observations39717
Missing cells30047
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 MiB
Average record size in memory328.0 B

Variable types

Numeric22
Categorical19

Warnings

int_rate has a high cardinality: 371 distinct values High cardinality
emp_title has a high cardinality: 28820 distinct values High cardinality
issue_d has a high cardinality: 55 distinct values High cardinality
title has a high cardinality: 19615 distinct values High cardinality
earliest_cr_line has a high cardinality: 526 distinct values High cardinality
revol_util has a high cardinality: 1089 distinct values High cardinality
last_pymnt_d has a high cardinality: 101 distinct values High cardinality
last_credit_pull_d has a high cardinality: 106 distinct values High cardinality
loan_amnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 3 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 3 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 2 other fieldsHigh correlation
out_prncp is highly correlated with out_prncp_invHigh correlation
out_prncp_inv is highly correlated with out_prncpHigh correlation
total_pymnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
total_pymnt_inv is highly correlated with funded_amnt_inv and 2 other fieldsHigh correlation
total_rec_prncp is highly correlated with total_pymnt and 1 other fieldsHigh correlation
sub_grade is highly correlated with gradeHigh correlation
grade is highly correlated with sub_gradeHigh correlation
emp_title has 2459 (6.2%) missing values Missing
emp_length has 1075 (2.7%) missing values Missing
mths_since_last_delinq has 25682 (64.7%) missing values Missing
pub_rec_bankruptcies has 697 (1.8%) missing values Missing
annual_inc is highly skewed (γ1 = 30.9491846) Skewed
collection_recovery_fee is highly skewed (γ1 = 25.02941842) Skewed
delinq_2yrs has 35405 (89.1%) zeros Zeros
inq_last_6mths has 19300 (48.6%) zeros Zeros
mths_since_last_delinq has 443 (1.1%) zeros Zeros
revol_bal has 994 (2.5%) zeros Zeros
out_prncp has 38577 (97.1%) zeros Zeros
out_prncp_inv has 38577 (97.1%) zeros Zeros
total_rec_late_fee has 37671 (94.8%) zeros Zeros
recoveries has 35499 (89.4%) zeros Zeros
collection_recovery_fee has 35935 (90.5%) zeros Zeros

Reproduction

Analysis started2021-04-14 06:00:34.455497
Analysis finished2021-04-14 06:02:53.133444
Duration2 minutes and 18.68 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct885
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11219.44381
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:32:53.360398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7456.670694
Coefficient of variation (CV)0.6646203517
Kurtosis0.7686685518
Mean11219.44381
Median Absolute Deviation (MAD)5000
Skewness1.05931729
Sum445602650
Variance55601937.84
MonotocityNot monotonic
2021-04-14T11:32:53.560708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002833
 
7.1%
120002334
 
5.9%
50002051
 
5.2%
60001908
 
4.8%
150001895
 
4.8%
200001626
 
4.1%
80001586
 
4.0%
250001390
 
3.5%
40001130
 
2.8%
30001030
 
2.6%
Other values (875)21934
55.2%
ValueCountFrequency (%)
5005
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
ValueCountFrequency (%)
35000679
1.7%
348002
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%
344755
 
< 0.1%

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1041
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10947.7132
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:32:53.782788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15400
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation7187.23867
Coefficient of variation (CV)0.6565059334
Kurtosis0.9375519943
Mean10947.7132
Median Absolute Deviation (MAD)4600
Skewness1.081710238
Sum434810325
Variance51656399.7
MonotocityNot monotonic
2021-04-14T11:32:53.992950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002741
 
6.9%
120002244
 
5.6%
50002040
 
5.1%
60001898
 
4.8%
150001784
 
4.5%
80001573
 
4.0%
200001456
 
3.7%
250001133
 
2.9%
40001127
 
2.8%
30001022
 
2.6%
Other values (1031)22699
57.2%
ValueCountFrequency (%)
5005
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
ValueCountFrequency (%)
35000554
1.4%
348001
 
< 0.1%
346752
 
< 0.1%
345251
 
< 0.1%
344754
 
< 0.1%

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8205
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10397.44887
Minimum0
Maximum35000
Zeros129
Zeros (%)0.3%
Memory size310.4 KiB
2021-04-14T11:32:54.211152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1873.658
Q15000
median8975
Q314400
95-th percentile24736.57226
Maximum35000
Range35000
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation7128.450439
Coefficient of variation (CV)0.6855961044
Kurtosis1.062544362
Mean10397.44887
Median Absolute Deviation (MAD)4200
Skewness1.106212938
Sum412955476.7
Variance50814805.66
MonotocityNot monotonic
2021-04-14T11:32:54.414312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001309
 
3.3%
100001275
 
3.2%
60001200
 
3.0%
120001069
 
2.7%
8000900
 
2.3%
4000812
 
2.0%
3000803
 
2.0%
15000657
 
1.7%
7000600
 
1.5%
2000452
 
1.1%
Other values (8195)30640
77.1%
ValueCountFrequency (%)
0129
0.3%
0.0001210981
 
< 0.1%
0.0005311331
 
< 0.1%
0.0006546071
 
< 0.1%
0.0018676961
 
< 0.1%
ValueCountFrequency (%)
35000135
0.3%
34997.352451
 
< 0.1%
34993.655391
 
< 0.1%
34993.325711
 
< 0.1%
34993.263061
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
36 months
29096 
60 months
10621 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months
ValueCountFrequency (%)
36 months29096
73.3%
60 months10621
 
26.7%
2021-04-14T11:32:55.693833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:32:55.800478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
months39717
50.0%
3629096
36.6%
6010621
 
13.4%

Most occurring characters

ValueCountFrequency (%)
79434
20.0%
639717
10.0%
m39717
10.0%
o39717
10.0%
n39717
10.0%
t39717
10.0%
h39717
10.0%
s39717
10.0%
329096
 
7.3%
010621
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter238302
60.0%
Space Separator79434
 
20.0%
Decimal Number79434
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
m39717
16.7%
o39717
16.7%
n39717
16.7%
t39717
16.7%
h39717
16.7%
s39717
16.7%
ValueCountFrequency (%)
639717
50.0%
329096
36.6%
010621
 
13.4%
ValueCountFrequency (%)
79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin238302
60.0%
Common158868
40.0%

Most frequent character per script

ValueCountFrequency (%)
m39717
16.7%
o39717
16.7%
n39717
16.7%
t39717
16.7%
h39717
16.7%
s39717
16.7%
ValueCountFrequency (%)
79434
50.0%
639717
25.0%
329096
 
18.3%
010621
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII397170
100.0%

Most frequent character per block

ValueCountFrequency (%)
79434
20.0%
639717
10.0%
m39717
10.0%
o39717
10.0%
n39717
10.0%
t39717
10.0%
h39717
10.0%
s39717
10.0%
329096
 
7.3%
010621
 
2.7%

int_rate
Categorical

HIGH CARDINALITY

Distinct371
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
10.99%
 
956
13.49%
 
826
11.49%
 
825
7.51%
 
787
7.88%
 
725
Other values (366)
35598 

Length

Max length6
Median length6
Mean length5.694287081
Min length5

Characters and Unicode

Total characters226160
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st row10.65%
2nd row15.27%
3rd row15.96%
4th row13.49%
5th row12.69%
ValueCountFrequency (%)
10.99%956
 
2.4%
13.49%826
 
2.1%
11.49%825
 
2.1%
7.51%787
 
2.0%
7.88%725
 
1.8%
7.49%656
 
1.7%
11.71%607
 
1.5%
9.99%603
 
1.5%
7.90%582
 
1.5%
5.42%573
 
1.4%
Other values (361)32577
82.0%
2021-04-14T11:32:56.153681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.99956
 
2.4%
13.49826
 
2.1%
11.49825
 
2.1%
7.51787
 
2.0%
7.88725
 
1.8%
7.49656
 
1.7%
11.71607
 
1.5%
9.99603
 
1.5%
7.90582
 
1.5%
5.42573
 
1.4%
Other values (361)32577
82.0%

Most occurring characters

ValueCountFrequency (%)
.39717
17.6%
%39717
17.6%
138195
16.9%
921893
9.7%
212734
 
5.6%
712132
 
5.4%
612033
 
5.3%
411091
 
4.9%
59947
 
4.4%
39929
 
4.4%
Other values (2)18772
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number146726
64.9%
Other Punctuation79434
35.1%

Most frequent character per category

ValueCountFrequency (%)
138195
26.0%
921893
14.9%
212734
 
8.7%
712132
 
8.3%
612033
 
8.2%
411091
 
7.6%
59947
 
6.8%
39929
 
6.8%
89527
 
6.5%
09245
 
6.3%
ValueCountFrequency (%)
.39717
50.0%
%39717
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common226160
100.0%

Most frequent character per script

ValueCountFrequency (%)
.39717
17.6%
%39717
17.6%
138195
16.9%
921893
9.7%
212734
 
5.6%
712132
 
5.4%
612033
 
5.3%
411091
 
4.9%
59947
 
4.4%
39929
 
4.4%
Other values (2)18772
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII226160
100.0%

Most frequent character per block

ValueCountFrequency (%)
.39717
17.6%
%39717
17.6%
138195
16.9%
921893
9.7%
212734
 
5.6%
712132
 
5.4%
612033
 
5.3%
411091
 
4.9%
59947
 
4.4%
39929
 
4.4%
Other values (2)18772
8.3%

installment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15383
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.5619221
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:32:56.326410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.246
Q1167.02
median280.22
Q3430.78
95-th percentile762.996
Maximum1305.19
Range1289.5
Interquartile range (IQR)263.76

Descriptive statistics

Standard deviation208.8748735
Coefficient of variation (CV)0.6435593929
Kurtosis1.246801303
Mean324.5619221
Median Absolute Deviation (MAD)123.2
Skewness1.128419095
Sum12890625.86
Variance43628.71279
MonotocityNot monotonic
2021-04-14T11:32:56.538067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1168
 
0.2%
180.9659
 
0.1%
311.0254
 
0.1%
150.848
 
0.1%
368.4546
 
0.1%
372.1245
 
0.1%
330.7643
 
0.1%
339.3142
 
0.1%
301.641
 
0.1%
317.7241
 
0.1%
Other values (15373)39230
98.8%
ValueCountFrequency (%)
15.691
< 0.1%
16.081
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
ValueCountFrequency (%)
1305.191
< 0.1%
1302.691
< 0.1%
1295.211
< 0.1%
1288.12
< 0.1%
1283.51
< 0.1%

grade
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B
12020 
A
10085 
C
8098 
D
5307 
E
2842 
Other values (2)
1365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB
ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%
2021-04-14T11:32:56.984348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:32:57.112511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
b12020
30.3%
a10085
25.4%
c8098
20.4%
d5307
13.4%
e2842
 
7.2%
f1049
 
2.6%
g316
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%

sub_grade
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B3
2917 
A4
2886 
A5
2742 
B5
2704 
B4
 
2512
Other values (30)
25956 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5
ValueCountFrequency (%)
B32917
 
7.3%
A42886
 
7.3%
A52742
 
6.9%
B52704
 
6.8%
B42512
 
6.3%
C12136
 
5.4%
B22057
 
5.2%
C22011
 
5.1%
B11830
 
4.6%
A31810
 
4.6%
Other values (25)16112
40.6%
2021-04-14T11:32:57.550344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b32917
 
7.3%
a42886
 
7.3%
a52742
 
6.9%
b52704
 
6.8%
b42512
 
6.3%
c12136
 
5.4%
b22057
 
5.2%
c22011
 
5.1%
b11830
 
4.6%
a31810
 
4.6%
Other values (25)16112
40.6%

Most occurring characters

ValueCountFrequency (%)
B12020
15.1%
A10085
12.7%
48293
10.4%
38215
10.3%
C8098
10.2%
58070
10.2%
27907
10.0%
17232
9.1%
D5307
6.7%
E2842
 
3.6%
Other values (2)1365
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39717
50.0%
Decimal Number39717
50.0%

Most frequent character per category

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%
ValueCountFrequency (%)
48293
20.9%
38215
20.7%
58070
20.3%
27907
19.9%
17232
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin39717
50.0%
Common39717
50.0%

Most frequent character per script

ValueCountFrequency (%)
B12020
30.3%
A10085
25.4%
C8098
20.4%
D5307
13.4%
E2842
 
7.2%
F1049
 
2.6%
G316
 
0.8%
ValueCountFrequency (%)
48293
20.9%
38215
20.7%
58070
20.3%
27907
19.9%
17232
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII79434
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12020
15.1%
A10085
12.7%
48293
10.4%
38215
10.3%
C8098
10.2%
58070
10.2%
27907
10.0%
17232
9.1%
D5307
6.7%
E2842
 
3.6%
Other values (2)1365
 
1.7%

emp_title
Categorical

HIGH CARDINALITY
MISSING

Distinct28820
Distinct (%)77.4%
Missing2459
Missing (%)6.2%
Memory size310.4 KiB
US Army
 
134
Bank of America
 
109
IBM
 
66
AT&T
 
59
Kaiser Permanente
 
56
Other values (28815)
36834 

Length

Max length78
Median length18
Mean length18.37978421
Min length2

Characters and Unicode

Total characters684794
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25641 ?
Unique (%)68.8%

Sample

1st rowRyder
2nd rowAIR RESOURCES BOARD
3rd rowUniversity Medical Group
4th rowVeolia Transportaton
5th rowSouthern Star Photography
ValueCountFrequency (%)
US Army134
 
0.3%
Bank of America109
 
0.3%
IBM66
 
0.2%
AT&T59
 
0.1%
Kaiser Permanente56
 
0.1%
Wells Fargo54
 
0.1%
USAF54
 
0.1%
UPS53
 
0.1%
US Air Force52
 
0.1%
Walmart45
 
0.1%
Other values (28810)36576
92.1%
(Missing)2459
 
6.2%
2021-04-14T11:32:58.171126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc3197
 
3.2%
of3008
 
3.0%
1208
 
1.2%
and963
 
1.0%
center818
 
0.8%
bank805
 
0.8%
county803
 
0.8%
services795
 
0.8%
school750
 
0.7%
the747
 
0.7%
Other values (18882)87491
87.0%

Most occurring characters

ValueCountFrequency (%)
64766
 
9.5%
e55954
 
8.2%
a43836
 
6.4%
n42641
 
6.2%
o42586
 
6.2%
i40491
 
5.9%
r40067
 
5.9%
t38580
 
5.6%
s30254
 
4.4%
l25923
 
3.8%
Other values (86)259696
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter489338
71.5%
Uppercase Letter119545
 
17.5%
Space Separator64766
 
9.5%
Other Punctuation8798
 
1.3%
Dash Punctuation1031
 
0.2%
Decimal Number968
 
0.1%
Open Punctuation159
 
< 0.1%
Close Punctuation156
 
< 0.1%
Math Symbol21
 
< 0.1%
Other Symbol2
 
< 0.1%
Other values (5)10
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C14579
 
12.2%
S13325
 
11.1%
A8885
 
7.4%
I7566
 
6.3%
M6518
 
5.5%
P6077
 
5.1%
T5691
 
4.8%
L5561
 
4.7%
E5241
 
4.4%
D5056
 
4.2%
Other values (18)41046
34.3%
ValueCountFrequency (%)
e55954
11.4%
a43836
9.0%
n42641
8.7%
o42586
8.7%
i40491
 
8.3%
r40067
 
8.2%
t38580
 
7.9%
s30254
 
6.2%
l25923
 
5.3%
c23099
 
4.7%
Other values (17)105907
21.6%
ValueCountFrequency (%)
.4253
48.3%
,2194
24.9%
&1301
 
14.8%
'652
 
7.4%
/311
 
3.5%
#36
 
0.4%
@10
 
0.1%
:9
 
0.1%
"8
 
0.1%
!8
 
0.1%
Other values (5)16
 
0.2%
ValueCountFrequency (%)
1192
19.8%
2161
16.6%
3155
16.0%
098
10.1%
491
9.4%
572
 
7.4%
962
 
6.4%
658
 
6.0%
746
 
4.8%
833
 
3.4%
ValueCountFrequency (%)
+18
85.7%
|2
 
9.5%
<1
 
4.8%
ValueCountFrequency (%)
(158
99.4%
[1
 
0.6%
ValueCountFrequency (%)
€1
50.0%
ƒ1
50.0%
ValueCountFrequency (%)
¢1
50.0%
$1
50.0%
ValueCountFrequency (%)
64766
100.0%
ValueCountFrequency (%)
-1031
100.0%
ValueCountFrequency (%)
)156
100.0%
ValueCountFrequency (%)
©2
100.0%
ValueCountFrequency (%)
`2
100.0%
ValueCountFrequency (%)
_2
100.0%
ValueCountFrequency (%)
²2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin608883
88.9%
Common75911
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
e55954
 
9.2%
a43836
 
7.2%
n42641
 
7.0%
o42586
 
7.0%
i40491
 
6.7%
r40067
 
6.6%
t38580
 
6.3%
s30254
 
5.0%
l25923
 
4.3%
c23099
 
3.8%
Other values (45)225452
37.0%
ValueCountFrequency (%)
64766
85.3%
.4253
 
5.6%
,2194
 
2.9%
&1301
 
1.7%
-1031
 
1.4%
'652
 
0.9%
/311
 
0.4%
1192
 
0.3%
2161
 
0.2%
(158
 
0.2%
Other values (31)892
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII684780
> 99.9%
None14
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
64766
 
9.5%
e55954
 
8.2%
a43836
 
6.4%
n42641
 
6.2%
o42586
 
6.2%
i40491
 
5.9%
r40067
 
5.9%
t38580
 
5.6%
s30254
 
4.4%
l25923
 
3.8%
Other values (77)259682
37.9%
ValueCountFrequency (%)
Ã3
21.4%
©2
14.3%
Â2
14.3%
²2
14.3%
â1
 
7.1%
€1
 
7.1%
¢1
 
7.1%
ƒ1
 
7.1%
¡1
 
7.1%

emp_length
Categorical

MISSING

Distinct11
Distinct (%)< 0.1%
Missing1075
Missing (%)2.7%
Memory size310.4 KiB
10+ years
8879 
< 1 year
4583 
2 years
4388 
3 years
4095 
4 years
3436 
Other values (6)
13261 

Length

Max length9
Median length7
Mean length7.494306713
Min length6

Characters and Unicode

Total characters289595
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row1 year
ValueCountFrequency (%)
10+ years8879
22.4%
< 1 year4583
11.5%
2 years4388
11.0%
3 years4095
10.3%
4 years3436
 
8.7%
5 years3282
 
8.3%
1 year3240
 
8.2%
6 years2229
 
5.6%
7 years1773
 
4.5%
8 years1479
 
3.7%
2021-04-14T11:32:58.685018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years30819
37.6%
108879
 
10.8%
17823
 
9.6%
year7823
 
9.6%
4583
 
5.6%
24388
 
5.4%
34095
 
5.0%
43436
 
4.2%
53282
 
4.0%
62229
 
2.7%
Other values (3)4510
 
5.5%

Most occurring characters

ValueCountFrequency (%)
43225
14.9%
y38642
13.3%
e38642
13.3%
a38642
13.3%
r38642
13.3%
s30819
10.6%
116702
 
5.8%
08879
 
3.1%
+8879
 
3.1%
<4583
 
1.6%
Other values (8)21940
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter185387
64.0%
Decimal Number47521
 
16.4%
Space Separator43225
 
14.9%
Math Symbol13462
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
116702
35.1%
08879
18.7%
24388
 
9.2%
34095
 
8.6%
43436
 
7.2%
53282
 
6.9%
62229
 
4.7%
71773
 
3.7%
81479
 
3.1%
91258
 
2.6%
ValueCountFrequency (%)
y38642
20.8%
e38642
20.8%
a38642
20.8%
r38642
20.8%
s30819
16.6%
ValueCountFrequency (%)
+8879
66.0%
<4583
34.0%
ValueCountFrequency (%)
43225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin185387
64.0%
Common104208
36.0%

Most frequent character per script

ValueCountFrequency (%)
43225
41.5%
116702
 
16.0%
08879
 
8.5%
+8879
 
8.5%
<4583
 
4.4%
24388
 
4.2%
34095
 
3.9%
43436
 
3.3%
53282
 
3.1%
62229
 
2.1%
Other values (3)4510
 
4.3%
ValueCountFrequency (%)
y38642
20.8%
e38642
20.8%
a38642
20.8%
r38642
20.8%
s30819
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII289595
100.0%

Most frequent character per block

ValueCountFrequency (%)
43225
14.9%
y38642
13.3%
e38642
13.3%
a38642
13.3%
r38642
13.3%
s30819
10.6%
116702
 
5.8%
08879
 
3.1%
+8879
 
3.1%
<4583
 
1.6%
Other values (8)21940
7.6%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
RENT
18899 
MORTGAGE
17659 
OWN
3058 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length4
Mean length5.703955485
Min length3

Characters and Unicode

Total characters226544
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT
ValueCountFrequency (%)
RENT18899
47.6%
MORTGAGE17659
44.5%
OWN3058
 
7.7%
OTHER98
 
0.2%
NONE3
 
< 0.1%
2021-04-14T11:32:59.077173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:32:59.223123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
rent18899
47.6%
mortgage17659
44.5%
own3058
 
7.7%
other98
 
0.2%
none3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter226544
100.0%

Most frequent character per category

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin226544
100.0%

Most frequent character per script

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII226544
100.0%

Most frequent character per block

ValueCountFrequency (%)
E36659
16.2%
R36656
16.2%
T36656
16.2%
G35318
15.6%
N21963
9.7%
O20818
9.2%
M17659
7.8%
A17659
7.8%
W3058
 
1.3%
H98
 
< 0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct5318
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68968.92638
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:32:59.387479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140404
median59000
Q382300
95-th percentile142000
Maximum6000000
Range5996000
Interquartile range (IQR)41896

Descriptive statistics

Standard deviation63793.76579
Coefficient of variation (CV)0.9249638807
Kurtosis2302.737777
Mean68968.92638
Median Absolute Deviation (MAD)20000
Skewness30.9491846
Sum2739238849
Variance4069644554
MonotocityNot monotonic
2021-04-14T11:32:59.624248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600001505
 
3.8%
500001057
 
2.7%
40000876
 
2.2%
45000830
 
2.1%
30000825
 
2.1%
75000811
 
2.0%
65000803
 
2.0%
70000733
 
1.8%
48000723
 
1.8%
80000662
 
1.7%
Other values (5308)30892
77.8%
ValueCountFrequency (%)
40001
 
< 0.1%
40801
 
< 0.1%
42002
< 0.1%
48004
< 0.1%
48881
 
< 0.1%
ValueCountFrequency (%)
60000001
< 0.1%
39000001
< 0.1%
20397841
< 0.1%
19000001
< 0.1%
17820001
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Not Verified
16921 
Verified
12809 
Source Verified
9987 

Length

Max length15
Median length12
Mean length11.46433517
Min length8

Characters and Unicode

Total characters455329
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified
ValueCountFrequency (%)
Not Verified16921
42.6%
Verified12809
32.3%
Source Verified9987
25.1%
2021-04-14T11:33:00.001121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:33:00.130365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
verified39717
59.6%
not16921
25.4%
source9987
 
15.0%

Most occurring characters

ValueCountFrequency (%)
e89421
19.6%
i79434
17.4%
r49704
10.9%
V39717
8.7%
f39717
8.7%
d39717
8.7%
o26908
 
5.9%
26908
 
5.9%
N16921
 
3.7%
t16921
 
3.7%
Other values (3)29961
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter361796
79.5%
Uppercase Letter66625
 
14.6%
Space Separator26908
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
e89421
24.7%
i79434
22.0%
r49704
13.7%
f39717
11.0%
d39717
11.0%
o26908
 
7.4%
t16921
 
4.7%
u9987
 
2.8%
c9987
 
2.8%
ValueCountFrequency (%)
V39717
59.6%
N16921
25.4%
S9987
 
15.0%
ValueCountFrequency (%)
26908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin428421
94.1%
Common26908
 
5.9%

Most frequent character per script

ValueCountFrequency (%)
e89421
20.9%
i79434
18.5%
r49704
11.6%
V39717
9.3%
f39717
9.3%
d39717
9.3%
o26908
 
6.3%
N16921
 
3.9%
t16921
 
3.9%
S9987
 
2.3%
Other values (2)19974
 
4.7%
ValueCountFrequency (%)
26908
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII455329
100.0%

Most frequent character per block

ValueCountFrequency (%)
e89421
19.6%
i79434
17.4%
r49704
10.9%
V39717
8.7%
f39717
8.7%
d39717
8.7%
o26908
 
5.9%
26908
 
5.9%
N16921
 
3.7%
t16921
 
3.7%
Other values (3)29961
 
6.6%

issue_d
Categorical

HIGH CARDINALITY

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Dec-11
 
2260
Nov-11
 
2223
Oct-11
 
2114
Sep-11
 
2063
Aug-11
 
1928
Other values (50)
29129 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238302
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDec-11
2nd rowDec-11
3rd rowDec-11
4th rowDec-11
5th rowDec-11
ValueCountFrequency (%)
Dec-112260
 
5.7%
Nov-112223
 
5.6%
Oct-112114
 
5.3%
Sep-112063
 
5.2%
Aug-111928
 
4.9%
Jul-111870
 
4.7%
Jun-111827
 
4.6%
May-111689
 
4.3%
Apr-111562
 
3.9%
Mar-111443
 
3.6%
Other values (45)20738
52.2%
2021-04-14T11:33:00.468373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-112260
 
5.7%
nov-112223
 
5.6%
oct-112114
 
5.3%
sep-112063
 
5.2%
aug-111928
 
4.9%
jul-111870
 
4.7%
jun-111827
 
4.6%
may-111689
 
4.3%
apr-111562
 
3.9%
mar-111443
 
3.6%
Other values (45)20738
52.2%

Most occurring characters

ValueCountFrequency (%)
154844
23.0%
-39717
16.7%
018061
 
7.6%
e10439
 
4.4%
u10273
 
4.3%
J9134
 
3.8%
c8367
 
3.5%
a8070
 
3.4%
p6482
 
2.7%
A6352
 
2.7%
Other values (18)66563
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79434
33.3%
Decimal Number79434
33.3%
Uppercase Letter39717
16.7%
Dash Punctuation39717
16.7%

Most frequent character per category

ValueCountFrequency (%)
e10439
13.1%
u10273
12.9%
c8367
10.5%
a8070
10.2%
p6482
8.2%
n5658
7.1%
r5526
7.0%
o4167
 
5.2%
v4167
 
5.2%
t3934
 
5.0%
Other values (4)12351
15.5%
ValueCountFrequency (%)
J9134
23.0%
A6352
16.0%
M5691
14.3%
D4433
11.2%
N4167
10.5%
O3934
9.9%
S3648
 
9.2%
F2358
 
5.9%
ValueCountFrequency (%)
154844
69.0%
018061
 
22.7%
94716
 
5.9%
81562
 
2.0%
7251
 
0.3%
ValueCountFrequency (%)
-39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119151
50.0%
Common119151
50.0%

Most frequent character per script

ValueCountFrequency (%)
e10439
 
8.8%
u10273
 
8.6%
J9134
 
7.7%
c8367
 
7.0%
a8070
 
6.8%
p6482
 
5.4%
A6352
 
5.3%
M5691
 
4.8%
n5658
 
4.7%
r5526
 
4.6%
Other values (12)43159
36.2%
ValueCountFrequency (%)
154844
46.0%
-39717
33.3%
018061
 
15.2%
94716
 
4.0%
81562
 
1.3%
7251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII238302
100.0%

Most frequent character per block

ValueCountFrequency (%)
154844
23.0%
-39717
16.7%
018061
 
7.6%
e10439
 
4.4%
u10273
 
4.3%
J9134
 
3.8%
c8367
 
3.5%
a8070
 
3.4%
p6482
 
2.7%
A6352
 
2.7%
Other values (18)66563
27.9%

loan_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Fully Paid
32950 
Charged Off
5627 
Current
 
1140

Length

Max length11
Median length10
Mean length10.05556814
Min length7

Characters and Unicode

Total characters399377
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowCurrent
ValueCountFrequency (%)
Fully Paid32950
83.0%
Charged Off5627
 
14.2%
Current1140
 
2.9%
2021-04-14T11:33:00.826140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:33:00.952672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fully32950
42.1%
paid32950
42.1%
off5627
 
7.2%
charged5627
 
7.2%
current1140
 
1.5%

Most occurring characters

ValueCountFrequency (%)
l65900
16.5%
38577
9.7%
a38577
9.7%
d38577
9.7%
u34090
8.5%
F32950
8.3%
y32950
8.3%
P32950
8.3%
i32950
8.3%
f11254
 
2.8%
Other values (8)40602
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter282506
70.7%
Uppercase Letter78294
 
19.6%
Space Separator38577
 
9.7%

Most frequent character per category

ValueCountFrequency (%)
l65900
23.3%
a38577
13.7%
d38577
13.7%
u34090
12.1%
y32950
11.7%
i32950
11.7%
f11254
 
4.0%
r7907
 
2.8%
e6767
 
2.4%
h5627
 
2.0%
Other values (3)7907
 
2.8%
ValueCountFrequency (%)
F32950
42.1%
P32950
42.1%
C6767
 
8.6%
O5627
 
7.2%
ValueCountFrequency (%)
38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin360800
90.3%
Common38577
 
9.7%

Most frequent character per script

ValueCountFrequency (%)
l65900
18.3%
a38577
10.7%
d38577
10.7%
u34090
9.4%
F32950
9.1%
y32950
9.1%
P32950
9.1%
i32950
9.1%
f11254
 
3.1%
r7907
 
2.2%
Other values (7)32695
9.1%
ValueCountFrequency (%)
38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII399377
100.0%

Most frequent character per block

ValueCountFrequency (%)
l65900
16.5%
38577
9.7%
a38577
9.7%
d38577
9.7%
u34090
8.5%
F32950
8.3%
y32950
8.3%
P32950
8.3%
i32950
8.3%
f11254
 
2.8%
Other values (8)40602
10.2%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
debt_consolidation
18641 
credit_card
5130 
other
3993 
home_improvement
2976 
major_purchase
2187 
Other values (9)
6790 

Length

Max length18
Median length16
Mean length13.7361835
Min length3

Characters and Unicode

Total characters545560
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowother
ValueCountFrequency (%)
debt_consolidation18641
46.9%
credit_card5130
 
12.9%
other3993
 
10.1%
home_improvement2976
 
7.5%
major_purchase2187
 
5.5%
small_business1828
 
4.6%
car1549
 
3.9%
wedding947
 
2.4%
medical693
 
1.7%
moving583
 
1.5%
Other values (4)1190
 
3.0%
2021-04-14T11:33:01.354747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation18641
46.9%
credit_card5130
 
12.9%
other3993
 
10.1%
home_improvement2976
 
7.5%
major_purchase2187
 
5.5%
small_business1828
 
4.6%
car1549
 
3.9%
wedding947
 
2.4%
medical693
 
1.7%
moving583
 
1.5%
Other values (4)1190
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o69725
12.8%
d50454
9.2%
i50145
9.2%
t50087
9.2%
n44528
8.2%
e43568
 
8.0%
c34036
 
6.2%
a33730
 
6.2%
_30865
 
5.7%
s28521
 
5.2%
Other values (12)109901
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter514695
94.3%
Connector Punctuation30865
 
5.7%

Most frequent character per category

ValueCountFrequency (%)
o69725
13.5%
d50454
9.8%
i50145
9.7%
t50087
9.7%
n44528
8.7%
e43568
8.5%
c34036
 
6.6%
a33730
 
6.6%
s28521
 
5.5%
l23418
 
4.5%
Other values (11)86483
16.8%
ValueCountFrequency (%)
_30865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin514695
94.3%
Common30865
 
5.7%

Most frequent character per script

ValueCountFrequency (%)
o69725
13.5%
d50454
9.8%
i50145
9.7%
t50087
9.7%
n44528
8.7%
e43568
8.5%
c34036
 
6.6%
a33730
 
6.6%
s28521
 
5.5%
l23418
 
4.5%
Other values (11)86483
16.8%
ValueCountFrequency (%)
_30865
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII545560
100.0%

Most frequent character per block

ValueCountFrequency (%)
o69725
12.8%
d50454
9.2%
i50145
9.2%
t50087
9.2%
n44528
8.2%
e43568
 
8.0%
c34036
 
6.2%
a33730
 
6.2%
_30865
 
5.7%
s28521
 
5.2%
Other values (12)109901
20.1%

title
Categorical

HIGH CARDINALITY

Distinct19615
Distinct (%)49.4%
Missing11
Missing (%)< 0.1%
Memory size310.4 KiB
Debt Consolidation
 
2184
Debt Consolidation Loan
 
1729
Personal Loan
 
659
Consolidation
 
517
debt consolidation
 
505
Other values (19610)
34112 

Length

Max length80
Median length16
Mean length17.18732685
Min length1

Characters and Unicode

Total characters682440
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17624 ?
Unique (%)44.4%

Sample

1st rowComputer
2nd rowbike
3rd rowreal estate business
4th rowpersonel
5th rowPersonal
ValueCountFrequency (%)
Debt Consolidation2184
 
5.5%
Debt Consolidation Loan1729
 
4.4%
Personal Loan659
 
1.7%
Consolidation517
 
1.3%
debt consolidation505
 
1.3%
Credit Card Consolidation356
 
0.9%
Home Improvement356
 
0.9%
Debt consolidation334
 
0.8%
Small Business Loan328
 
0.8%
Credit Card Loan317
 
0.8%
Other values (19605)32421
81.6%
2021-04-14T11:33:01.859593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan10895
 
10.4%
debt9245
 
8.8%
consolidation8622
 
8.2%
credit4604
 
4.4%
card3341
 
3.2%
personal2043
 
2.0%
home1875
 
1.8%
pay1344
 
1.3%
off1259
 
1.2%
my1133
 
1.1%
Other values (8935)60203
57.6%

Most occurring characters

ValueCountFrequency (%)
66029
 
9.7%
o65729
 
9.6%
n55657
 
8.2%
e54557
 
8.0%
a50167
 
7.4%
i43822
 
6.4%
t42600
 
6.2%
d30679
 
4.5%
r29153
 
4.3%
s28544
 
4.2%
Other values (98)215503
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter521300
76.4%
Uppercase Letter83242
 
12.2%
Space Separator66029
 
9.7%
Decimal Number5995
 
0.9%
Other Punctuation4442
 
0.7%
Dash Punctuation824
 
0.1%
Connector Punctuation213
 
< 0.1%
Close Punctuation104
 
< 0.1%
Currency Symbol94
 
< 0.1%
Math Symbol92
 
< 0.1%
Other values (5)105
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C18509
22.2%
L10335
12.4%
D9244
11.1%
P5641
 
6.8%
R3732
 
4.5%
M3256
 
3.9%
S3227
 
3.9%
B3116
 
3.7%
H2910
 
3.5%
I2885
 
3.5%
Other values (18)20387
24.5%
ValueCountFrequency (%)
o65729
12.6%
n55657
10.7%
e54557
10.5%
a50167
9.6%
i43822
8.4%
t42600
8.2%
d30679
 
5.9%
r29153
 
5.6%
s28544
 
5.5%
l26300
 
5.0%
Other values (18)94092
18.0%
ValueCountFrequency (%)
!1123
25.3%
'982
22.1%
.712
16.0%
/538
12.1%
,435
 
9.8%
&328
 
7.4%
%95
 
2.1%
:64
 
1.4%
"56
 
1.3%
#25
 
0.6%
Other values (5)84
 
1.9%
ValueCountFrequency (%)
11691
28.2%
01677
28.0%
21105
18.4%
3299
 
5.0%
5256
 
4.3%
9254
 
4.2%
4216
 
3.6%
6178
 
3.0%
8169
 
2.8%
7150
 
2.5%
ValueCountFrequency (%)
€4
21.1%
4
21.1%
—4
21.1%
2
10.5%
™2
10.5%
–1
 
5.3%
‚1
 
5.3%
…1
 
5.3%
ValueCountFrequency (%)
+53
57.6%
=19
 
20.7%
<9
 
9.8%
>8
 
8.7%
~2
 
2.2%
|1
 
1.1%
ValueCountFrequency (%)
^1
33.3%
´1
33.3%
`1
33.3%
ValueCountFrequency (%)
(77
96.2%
[3
 
3.8%
ValueCountFrequency (%)
)100
96.2%
]4
 
3.8%
ValueCountFrequency (%)
66029
100.0%
ValueCountFrequency (%)
-824
100.0%
ValueCountFrequency (%)
_213
100.0%
ValueCountFrequency (%)
$94
100.0%
ValueCountFrequency (%)
³1
100.0%
ValueCountFrequency (%)
¦2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin604542
88.6%
Common77898
 
11.4%

Most frequent character per script

ValueCountFrequency (%)
o65729
 
10.9%
n55657
 
9.2%
e54557
 
9.0%
a50167
 
8.3%
i43822
 
7.2%
t42600
 
7.0%
d30679
 
5.1%
r29153
 
4.8%
s28544
 
4.7%
l26300
 
4.4%
Other values (46)177334
29.3%
ValueCountFrequency (%)
66029
84.8%
11691
 
2.2%
01677
 
2.2%
!1123
 
1.4%
21105
 
1.4%
'982
 
1.3%
-824
 
1.1%
.712
 
0.9%
/538
 
0.7%
,435
 
0.6%
Other values (42)2782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII682408
> 99.9%
None32
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
66029
 
9.7%
o65729
 
9.6%
n55657
 
8.2%
e54557
 
8.0%
a50167
 
7.4%
i43822
 
6.4%
t42600
 
6.2%
d30679
 
4.5%
r29153
 
4.3%
s28544
 
4.2%
Other values (84)215471
31.6%
ValueCountFrequency (%)
â4
12.5%
€4
12.5%
î4
12.5%
4
12.5%
—4
12.5%
Ã2
6.2%
™2
6.2%
¦2
6.2%
–1
 
3.1%
‚1
 
3.1%
Other values (4)4
12.5%

addr_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
CA
7099 
NY
3812 
FL
2866 
TX
2727 
NJ
 
1850
Other values (45)
21363 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR
ValueCountFrequency (%)
CA7099
17.9%
NY3812
 
9.6%
FL2866
 
7.2%
TX2727
 
6.9%
NJ1850
 
4.7%
IL1525
 
3.8%
PA1517
 
3.8%
VA1407
 
3.5%
GA1398
 
3.5%
MA1340
 
3.4%
Other values (40)14176
35.7%
2021-04-14T11:33:02.305622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca7099
17.9%
ny3812
 
9.6%
fl2866
 
7.2%
tx2727
 
6.9%
nj1850
 
4.7%
il1525
 
3.8%
pa1517
 
3.8%
va1407
 
3.5%
ga1398
 
3.5%
ma1340
 
3.4%
Other values (40)14176
35.7%

Most occurring characters

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter79434
100.0%

Most frequent character per category

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin79434
100.0%

Most frequent character per script

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII79434
100.0%

Most frequent character per block

ValueCountFrequency (%)
A15698
19.8%
C10116
12.7%
N7953
10.0%
L5279
 
6.6%
M4706
 
5.9%
Y4220
 
5.3%
T3892
 
4.9%
O3451
 
4.3%
I3097
 
3.9%
F2866
 
3.6%
Other values (14)18156
22.9%

dti
Real number (ℝ≥0)

Distinct2868
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.31512954
Minimum0
Maximum29.99
Zeros183
Zeros (%)0.5%
Memory size310.4 KiB
2021-04-14T11:33:02.482494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.13
Q18.17
median13.4
Q318.6
95-th percentile23.84
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.678593595
Coefficient of variation (CV)0.501579318
Kurtosis-0.8520154806
Mean13.31512954
Median Absolute Deviation (MAD)5.21
Skewness-0.02804333095
Sum528837
Variance44.6036124
MonotocityNot monotonic
2021-04-14T11:33:02.682316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0183
 
0.5%
1251
 
0.1%
1845
 
0.1%
19.240
 
0.1%
13.239
 
0.1%
16.838
 
0.1%
12.4838
 
0.1%
13.538
 
0.1%
637
 
0.1%
14.2936
 
0.1%
Other values (2858)39172
98.6%
ValueCountFrequency (%)
0183
0.5%
0.013
 
< 0.1%
0.025
 
< 0.1%
0.032
 
< 0.1%
0.043
 
< 0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.951
 
< 0.1%
29.933
< 0.1%
29.922
< 0.1%
29.891
 
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1465115694
Minimum0
Maximum11
Zeros35405
Zeros (%)89.1%
Memory size310.4 KiB
2021-04-14T11:33:02.861229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.491811516
Coefficient of variation (CV)3.356810102
Kurtosis39.41249957
Mean0.1465115694
Median Absolute Deviation (MAD)0
Skewness5.022035213
Sum5819
Variance0.2418785673
MonotocityNot monotonic
2021-04-14T11:33:03.050577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
035405
89.1%
13303
 
8.3%
2687
 
1.7%
3220
 
0.6%
462
 
0.2%
522
 
0.1%
610
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
035405
89.1%
13303
 
8.3%
2687
 
1.7%
3220
 
0.6%
462
 
0.2%
ValueCountFrequency (%)
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
610
< 0.1%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct526
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Nov-98
 
370
Oct-99
 
366
Dec-98
 
348
Oct-00
 
346
Dec-97
 
329
Other values (521)
37958 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238302
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.1%

Sample

1st rowJan-85
2nd rowApr-99
3rd rowNov-01
4th rowFeb-96
5th rowJan-96
ValueCountFrequency (%)
Nov-98370
 
0.9%
Oct-99366
 
0.9%
Dec-98348
 
0.9%
Oct-00346
 
0.9%
Dec-97329
 
0.8%
Nov-00320
 
0.8%
Nov-99319
 
0.8%
Sep-00306
 
0.8%
Oct-98305
 
0.8%
Nov-97298
 
0.8%
Other values (516)36410
91.7%
2021-04-14T11:33:03.486155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nov-98370
 
0.9%
oct-99366
 
0.9%
dec-98348
 
0.9%
oct-00346
 
0.9%
dec-97329
 
0.8%
nov-00320
 
0.8%
nov-99319
 
0.8%
sep-00306
 
0.8%
oct-98305
 
0.8%
nov-97298
 
0.8%
Other values (516)36410
91.7%

Most occurring characters

ValueCountFrequency (%)
-39717
16.7%
923353
 
9.8%
019365
 
8.1%
e10541
 
4.4%
J9426
 
4.0%
u9302
 
3.9%
a9126
 
3.8%
88453
 
3.5%
c8143
 
3.4%
n6364
 
2.7%
Other values (23)94512
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79434
33.3%
Decimal Number79434
33.3%
Uppercase Letter39717
16.7%
Dash Punctuation39717
16.7%

Most frequent character per category

ValueCountFrequency (%)
e10541
13.3%
u9302
11.7%
a9126
11.5%
c8143
10.3%
n6364
8.0%
p6335
8.0%
r5536
7.0%
t4076
 
5.1%
o3930
 
4.9%
v3930
 
4.9%
Other values (4)12151
15.3%
ValueCountFrequency (%)
923353
29.4%
019365
24.4%
88453
 
10.6%
74822
 
6.1%
44274
 
5.4%
54201
 
5.3%
64174
 
5.3%
33784
 
4.8%
13736
 
4.7%
23272
 
4.1%
ValueCountFrequency (%)
J9426
23.7%
A6047
15.2%
M5697
14.3%
O4076
10.3%
D4067
10.2%
N3930
9.9%
S3593
 
9.0%
F2881
 
7.3%
ValueCountFrequency (%)
-39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119151
50.0%
Common119151
50.0%

Most frequent character per script

ValueCountFrequency (%)
e10541
 
8.8%
J9426
 
7.9%
u9302
 
7.8%
a9126
 
7.7%
c8143
 
6.8%
n6364
 
5.3%
p6335
 
5.3%
A6047
 
5.1%
M5697
 
4.8%
r5536
 
4.6%
Other values (12)42634
35.8%
ValueCountFrequency (%)
-39717
33.3%
923353
19.6%
019365
16.3%
88453
 
7.1%
74822
 
4.0%
44274
 
3.6%
54201
 
3.5%
64174
 
3.5%
33784
 
3.2%
13736
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII238302
100.0%

Most frequent character per block

ValueCountFrequency (%)
-39717
16.7%
923353
 
9.8%
019365
 
8.1%
e10541
 
4.4%
J9426
 
4.0%
u9302
 
3.9%
a9126
 
3.8%
88453
 
3.5%
c8143
 
3.4%
n6364
 
2.7%
Other values (23)94512
39.7%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8691995871
Minimum0
Maximum8
Zeros19300
Zeros (%)48.6%
Memory size310.4 KiB
2021-04-14T11:33:03.632441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.070219332
Coefficient of variation (CV)1.23126995
Kurtosis2.562159858
Mean0.8691995871
Median Absolute Deviation (MAD)1
Skewness1.390390927
Sum34522
Variance1.145369419
MonotocityNot monotonic
2021-04-14T11:33:03.788519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
019300
48.6%
110971
27.6%
25812
 
14.6%
33048
 
7.7%
4326
 
0.8%
5146
 
0.4%
664
 
0.2%
735
 
0.1%
815
 
< 0.1%
ValueCountFrequency (%)
019300
48.6%
110971
27.6%
25812
 
14.6%
33048
 
7.7%
4326
 
0.8%
ValueCountFrequency (%)
815
 
< 0.1%
735
 
0.1%
664
 
0.2%
5146
0.4%
4326
0.8%

mths_since_last_delinq
Real number (ℝ≥0)

MISSING
ZEROS

Distinct95
Distinct (%)0.7%
Missing25682
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean35.90096188
Minimum0
Maximum120
Zeros443
Zeros (%)1.1%
Memory size310.4 KiB
2021-04-14T11:33:03.984531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median34
Q352
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.02005955
Coefficient of variation (CV)0.6133556984
Kurtosis-0.8425777778
Mean35.90096188
Median Absolute Deviation (MAD)17
Skewness0.3064368727
Sum503870
Variance484.8830224
MonotocityNot monotonic
2021-04-14T11:33:04.196790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0443
 
1.1%
15252
 
0.6%
23247
 
0.6%
30247
 
0.6%
24241
 
0.6%
19238
 
0.6%
38237
 
0.6%
20233
 
0.6%
22231
 
0.6%
18231
 
0.6%
Other values (85)11435
28.8%
(Missing)25682
64.7%
ValueCountFrequency (%)
0443
1.1%
130
 
0.1%
2101
 
0.3%
3145
 
0.4%
4153
 
0.4%
ValueCountFrequency (%)
1201
< 0.1%
1151
< 0.1%
1071
< 0.1%
1061
< 0.1%
1032
< 0.1%

open_acc
Real number (ℝ≥0)

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.294407936
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:33:04.411291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.400282474
Coefficient of variation (CV)0.4734333272
Kurtosis1.67757203
Mean9.294407936
Median Absolute Deviation (MAD)3
Skewness1.00376191
Sum369146
Variance19.36248585
MonotocityNot monotonic
2021-04-14T11:33:04.602459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
74018
10.1%
63946
9.9%
83936
9.9%
93718
9.4%
103223
 
8.1%
53183
 
8.0%
112746
 
6.9%
42343
 
5.9%
122273
 
5.7%
131911
 
4.8%
Other values (30)8420
21.2%
ValueCountFrequency (%)
2605
 
1.5%
31493
 
3.8%
42343
5.9%
53183
8.0%
63946
9.9%
ValueCountFrequency (%)
441
< 0.1%
421
< 0.1%
411
< 0.1%
391
< 0.1%
381
< 0.1%

pub_rec
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
37601 
1
 
2056
2
 
51
3
 
7
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%
2021-04-14T11:33:04.976032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:33:05.092589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39717
100.0%

Most frequent character per category

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common39717
100.0%

Most frequent character per script

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII39717
100.0%

Most frequent character per block

ValueCountFrequency (%)
037601
94.7%
12056
 
5.2%
251
 
0.1%
37
 
< 0.1%
42
 
< 0.1%

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct21711
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13382.52809
Minimum0
Maximum149588
Zeros994
Zeros (%)2.5%
Memory size310.4 KiB
2021-04-14T11:33:05.254839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile321.8
Q13703
median8850
Q317058
95-th percentile41656.4
Maximum149588
Range149588
Interquartile range (IQR)13355

Descriptive statistics

Standard deviation15885.01664
Coefficient of variation (CV)1.186996697
Kurtosis14.89652278
Mean13382.52809
Median Absolute Deviation (MAD)6027
Skewness3.190883683
Sum531513868
Variance252333753.7
MonotocityNot monotonic
2021-04-14T11:33:05.456373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0994
 
2.5%
25514
 
< 0.1%
29814
 
< 0.1%
112
 
< 0.1%
68211
 
< 0.1%
7989
 
< 0.1%
3469
 
< 0.1%
109
 
< 0.1%
8659
 
< 0.1%
529
 
< 0.1%
Other values (21701)38627
97.3%
ValueCountFrequency (%)
0994
2.5%
112
 
< 0.1%
25
 
< 0.1%
36
 
< 0.1%
43
 
< 0.1%
ValueCountFrequency (%)
1495881
< 0.1%
1495271
< 0.1%
1490001
< 0.1%
1488291
< 0.1%
1488041
< 0.1%

revol_util
Categorical

HIGH CARDINALITY

Distinct1089
Distinct (%)2.7%
Missing50
Missing (%)0.1%
Memory size310.4 KiB
0%
 
977
0.20%
 
63
63%
 
62
66.70%
 
58
0.10%
 
58
Other values (1084)
38449 

Length

Max length6
Median length6
Mean length5.521919984
Min length2

Characters and Unicode

Total characters219038
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.2%

Sample

1st row83.70%
2nd row9.40%
3rd row98.50%
4th row21%
5th row53.90%
ValueCountFrequency (%)
0%977
 
2.5%
0.20%63
 
0.2%
63%62
 
0.2%
66.70%58
 
0.1%
0.10%58
 
0.1%
40.70%58
 
0.1%
46.40%57
 
0.1%
31.20%57
 
0.1%
66.60%57
 
0.1%
61%57
 
0.1%
Other values (1079)38163
96.1%
2021-04-14T11:33:05.940164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0977
 
2.5%
0.2063
 
0.2%
6362
 
0.2%
40.7058
 
0.1%
0.1058
 
0.1%
66.7058
 
0.1%
66.6057
 
0.1%
6157
 
0.1%
31.2057
 
0.1%
46.4057
 
0.1%
Other values (1079)38163
96.2%

Most occurring characters

ValueCountFrequency (%)
039671
18.1%
%39667
18.1%
.34841
15.9%
412082
 
5.5%
512063
 
5.5%
611989
 
5.5%
711949
 
5.5%
311885
 
5.4%
211550
 
5.3%
811419
 
5.2%
Other values (2)21922
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number144530
66.0%
Other Punctuation74508
34.0%

Most frequent character per category

ValueCountFrequency (%)
039671
27.4%
412082
 
8.4%
512063
 
8.3%
611989
 
8.3%
711949
 
8.3%
311885
 
8.2%
211550
 
8.0%
811419
 
7.9%
111111
 
7.7%
910811
 
7.5%
ValueCountFrequency (%)
%39667
53.2%
.34841
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common219038
100.0%

Most frequent character per script

ValueCountFrequency (%)
039671
18.1%
%39667
18.1%
.34841
15.9%
412082
 
5.5%
512063
 
5.5%
611989
 
5.5%
711949
 
5.5%
311885
 
5.4%
211550
 
5.3%
811419
 
5.2%
Other values (2)21922
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII219038
100.0%

Most frequent character per block

ValueCountFrequency (%)
039671
18.1%
%39667
18.1%
.34841
15.9%
412082
 
5.5%
512063
 
5.5%
611989
 
5.5%
711949
 
5.5%
311885
 
5.4%
211550
 
5.3%
811419
 
5.2%
Other values (2)21922
10.0%

total_acc
Real number (ℝ≥0)

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.08882846
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Memory size310.4 KiB
2021-04-14T11:33:06.148183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.40170855
Coefficient of variation (CV)0.5161753405
Kurtosis0.6937402027
Mean22.08882846
Median Absolute Deviation (MAD)7
Skewness0.8273790855
Sum877302
Variance129.9989579
MonotocityNot monotonic
2021-04-14T11:33:06.333744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161471
 
3.7%
151462
 
3.7%
171457
 
3.7%
141445
 
3.6%
201428
 
3.6%
181422
 
3.6%
211412
 
3.6%
131385
 
3.5%
191341
 
3.4%
121325
 
3.3%
Other values (72)25569
64.4%
ValueCountFrequency (%)
24
 
< 0.1%
3182
 
0.5%
4420
1.1%
5552
1.4%
6683
1.7%
ValueCountFrequency (%)
901
< 0.1%
871
< 0.1%
811
< 0.1%
801
< 0.1%
792
< 0.1%

out_prncp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1137
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.2278873
Minimum0
Maximum6311.47
Zeros38577
Zeros (%)97.1%
Memory size310.4 KiB
2021-04-14T11:33:06.534309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6311.47
Range6311.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation375.1728389
Coefficient of variation (CV)7.323605532
Kurtosis97.6585546
Mean51.2278873
Median Absolute Deviation (MAD)0
Skewness9.226730006
Sum2034618
Variance140754.659
MonotocityNot monotonic
2021-04-14T11:33:06.757408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038577
97.1%
2277.112
 
< 0.1%
2963.242
 
< 0.1%
827.132
 
< 0.1%
1972.62
 
< 0.1%
1202.051
 
< 0.1%
4316.131
 
< 0.1%
3006.671
 
< 0.1%
1725.341
 
< 0.1%
743.521
 
< 0.1%
Other values (1127)1127
 
2.8%
ValueCountFrequency (%)
038577
97.1%
10.261
 
< 0.1%
11.911
 
< 0.1%
13.281
 
< 0.1%
19.121
 
< 0.1%
ValueCountFrequency (%)
6311.471
< 0.1%
6308.371
< 0.1%
6307.371
< 0.1%
6307.151
< 0.1%
6219.161
< 0.1%

out_prncp_inv
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1138
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.98976811
Minimum0
Maximum6307.37
Zeros38577
Zeros (%)97.1%
Memory size310.4 KiB
2021-04-14T11:33:06.967533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6307.37
Range6307.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation373.8244569
Coefficient of variation (CV)7.331362169
Kurtosis98.04055348
Mean50.98976811
Median Absolute Deviation (MAD)0
Skewness9.243765495
Sum2025160.62
Variance139744.7246
MonotocityNot monotonic
2021-04-14T11:33:07.170366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038577
97.1%
1972.62
 
< 0.1%
827.132
 
< 0.1%
1664.642
 
< 0.1%
1212.391
 
< 0.1%
1662.571
 
< 0.1%
3335.411
 
< 0.1%
3131.631
 
< 0.1%
272.651
 
< 0.1%
87.941
 
< 0.1%
Other values (1128)1128
 
2.8%
ValueCountFrequency (%)
038577
97.1%
10.261
 
< 0.1%
11.911
 
< 0.1%
13.281
 
< 0.1%
19.091
 
< 0.1%
ValueCountFrequency (%)
6307.371
< 0.1%
6306.961
< 0.1%
6298.111
< 0.1%
6276.751
< 0.1%
6219.161
< 0.1%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37850
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12153.59654
Minimum0
Maximum58563.67993
Zeros16
Zeros (%)< 0.1%
Memory size310.4 KiB
2021-04-14T11:33:07.402321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1887.957036
Q15576.93
median9899.640319
Q316534.43304
95-th percentile30245.11853
Maximum58563.67993
Range58563.67993
Interquartile range (IQR)10957.50304

Descriptive statistics

Standard deviation9042.040766
Coefficient of variation (CV)0.743980659
Kurtosis1.985894249
Mean12153.59654
Median Absolute Deviation (MAD)5016.756711
Skewness1.339857366
Sum482704393.9
Variance81758501.21
MonotocityNot monotonic
2021-04-14T11:33:07.598933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.5694326
 
0.1%
016
 
< 0.1%
10956.7759616
 
< 0.1%
11784.2322316
 
< 0.1%
5478.38798115
 
< 0.1%
13148.1378615
 
< 0.1%
5557.02554313
 
< 0.1%
13435.9002113
 
< 0.1%
13263.9546412
 
< 0.1%
14288.7616911
 
< 0.1%
Other values (37840)39564
99.6%
ValueCountFrequency (%)
016
< 0.1%
33.731
 
< 0.1%
35.711
 
< 0.1%
44.922
 
< 0.1%
44.961
 
< 0.1%
ValueCountFrequency (%)
58563.679931
< 0.1%
58480.139921
< 0.1%
57835.279911
< 0.1%
56849.269861
< 0.1%
56662.589941
< 0.1%

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37518
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11567.14912
Minimum0
Maximum58563.68
Zeros165
Zeros (%)0.4%
Memory size310.4 KiB
2021-04-14T11:33:07.884842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1420.408
Q15112.31
median9287.15
Q315798.81
95-th percentile29627.236
Maximum58563.68
Range58563.68
Interquartile range (IQR)10686.5

Descriptive statistics

Standard deviation8942.672613
Coefficient of variation (CV)0.7731094777
Kurtosis2.029758507
Mean11567.14912
Median Absolute Deviation (MAD)4939.58
Skewness1.35483764
Sum459412461.5
Variance79971393.47
MonotocityNot monotonic
2021-04-14T11:33:08.111232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0165
 
0.4%
6514.5216
 
< 0.1%
5478.3914
 
< 0.1%
13148.1414
 
< 0.1%
6717.9512
 
< 0.1%
10956.7812
 
< 0.1%
11196.5712
 
< 0.1%
5557.0311
 
< 0.1%
7328.9211
 
< 0.1%
13517.3611
 
< 0.1%
Other values (37508)39439
99.3%
ValueCountFrequency (%)
0165
0.4%
0.541
 
< 0.1%
12.651
 
< 0.1%
18.971
 
< 0.1%
21.61
 
< 0.1%
ValueCountFrequency (%)
58563.681
< 0.1%
58438.371
< 0.1%
57628.731
< 0.1%
56622.121
< 0.1%
56515.161
< 0.1%

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7976
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9793.348813
Minimum0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Memory size310.4 KiB
2021-04-14T11:33:08.352648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1339.842
Q14600
median8000
Q313653.26
95-th percentile24999.982
Maximum35000.02
Range35000.02
Interquartile range (IQR)9053.26

Descriptive statistics

Standard deviation7065.522127
Coefficient of variation (CV)0.7214612961
Kurtosis1.103355455
Mean9793.348813
Median Absolute Deviation (MAD)4000
Skewness1.118254546
Sum388962434.8
Variance49921602.93
MonotocityNot monotonic
2021-04-14T11:33:08.570300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002293
 
5.8%
120001805
 
4.5%
50001702
 
4.3%
60001637
 
4.1%
150001400
 
3.5%
80001318
 
3.3%
200001059
 
2.7%
4000956
 
2.4%
3000883
 
2.2%
7000851
 
2.1%
Other values (7966)25813
65.0%
ValueCountFrequency (%)
074
0.2%
21.211
 
< 0.1%
21.931
 
< 0.1%
22.241
 
< 0.1%
22.51
 
< 0.1%
ValueCountFrequency (%)
35000.022
 
< 0.1%
35000.011
 
< 0.1%
35000363
0.9%
34999.995
 
< 0.1%
34999.981
 
< 0.1%

total_rec_int
Real number (ℝ≥0)

Distinct35148
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2263.663172
Minimum0
Maximum23563.68
Zeros71
Zeros (%)0.2%
Memory size310.4 KiB
2021-04-14T11:33:09.018297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.168
Q1662.18
median1348.91
Q32833.4
95-th percentile7575.812
Maximum23563.68
Range23563.68
Interquartile range (IQR)2171.22

Descriptive statistics

Standard deviation2608.111964
Coefficient of variation (CV)1.152164331
Kurtosis9.688278395
Mean2263.663172
Median Absolute Deviation (MAD)866.01
Skewness2.668747187
Sum89905910.21
Variance6802248.019
MonotocityNot monotonic
2021-04-14T11:33:09.388443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071
 
0.2%
1196.5726
 
0.1%
514.5219
 
< 0.1%
1784.2317
 
< 0.1%
717.9517
 
< 0.1%
1148.1417
 
< 0.1%
956.7817
 
< 0.1%
478.3916
 
< 0.1%
1907.3514
 
< 0.1%
632.2113
 
< 0.1%
Other values (35138)39490
99.4%
ValueCountFrequency (%)
071
0.2%
6.221
 
< 0.1%
6.271
 
< 0.1%
7.191
 
< 0.1%
7.22
 
< 0.1%
ValueCountFrequency (%)
23563.681
< 0.1%
23506.561
< 0.1%
23480.141
< 0.1%
22835.281
< 0.1%
22716.421
< 0.1%

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct1356
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.363015212
Minimum0
Maximum180.2
Zeros37671
Zeros (%)94.8%
Memory size310.4 KiB
2021-04-14T11:33:09.823326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.924199
Maximum180.2
Range180.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.289979302
Coefficient of variation (CV)5.348421085
Kurtosis100.8515437
Mean1.363015212
Median Absolute Deviation (MAD)0
Skewness8.429536
Sum54134.87519
Variance53.14379822
MonotocityNot monotonic
2021-04-14T11:33:10.441228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037671
94.8%
15255
 
0.6%
15.0000000158
 
0.1%
3055
 
0.1%
15.0000000247
 
0.1%
14.9999999940
 
0.1%
14.9999999833
 
0.1%
15.0000000332
 
0.1%
15.0000000425
 
0.1%
14.9999999725
 
0.1%
Other values (1346)1476
 
3.7%
ValueCountFrequency (%)
037671
94.8%
0.011
 
< 0.1%
0.0607997511
 
< 0.1%
0.0737871041
 
< 0.1%
0.1017045621
 
< 0.1%
ValueCountFrequency (%)
180.21
< 0.1%
166.42971071
< 0.1%
165.691
< 0.1%
146.60000031
< 0.1%
146.041
< 0.1%

recoveries
Real number (ℝ≥0)

ZEROS

Distinct4040
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.22162387
Minimum0
Maximum29623.35
Zeros35499
Zeros (%)89.4%
Memory size310.4 KiB
2021-04-14T11:33:10.819201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile362.418
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation688.744771
Coefficient of variation (CV)7.23307105
Kurtosis379.3775773
Mean95.22162387
Median Absolute Deviation (MAD)0
Skewness16.5193782
Sum3781917.235
Variance474369.3595
MonotocityNot monotonic
2021-04-14T11:33:11.190014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035499
89.4%
10.44
 
< 0.1%
11.294
 
< 0.1%
10.663
 
< 0.1%
13.593
 
< 0.1%
13.933
 
< 0.1%
164.813
 
< 0.1%
19.23
 
< 0.1%
10.073
 
< 0.1%
10.133
 
< 0.1%
Other values (4030)4189
 
10.5%
ValueCountFrequency (%)
035499
89.4%
6.31
 
< 0.1%
6.311
 
< 0.1%
8.191
 
< 0.1%
8.361
 
< 0.1%
ValueCountFrequency (%)
29623.351
< 0.1%
22943.371
< 0.1%
21810.311
< 0.1%
20006.531
< 0.1%
19915.671
< 0.1%

collection_recovery_fee
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2616
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.40611189
Minimum0
Maximum7002.19
Zeros35935
Zeros (%)90.5%
Memory size310.4 KiB
2021-04-14T11:33:11.543548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.152
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation148.6715935
Coefficient of variation (CV)11.98373791
Kurtosis821.3006591
Mean12.40611189
Median Absolute Deviation (MAD)0
Skewness25.02941842
Sum492733.5461
Variance22103.2427
MonotocityNot monotonic
2021-04-14T11:33:11.753018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035935
90.5%
212
 
< 0.1%
1.210
 
< 0.1%
3.719
 
< 0.1%
1.888
 
< 0.1%
0.88
 
< 0.1%
1.698
 
< 0.1%
1.218
 
< 0.1%
2.028
 
< 0.1%
1.68
 
< 0.1%
Other values (2606)3703
 
9.3%
ValueCountFrequency (%)
035935
90.5%
0.0631
 
< 0.1%
0.0745000011
 
< 0.1%
0.1347999951
 
< 0.1%
0.13931
 
< 0.1%
ValueCountFrequency (%)
7002.191
< 0.1%
6972.591
< 0.1%
6543.041
< 0.1%
5774.81
< 0.1%
5602.721
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct101
Distinct (%)0.3%
Missing71
Missing (%)0.2%
Memory size310.4 KiB
May-16
 
1256
Mar-13
 
1026
Dec-14
 
945
May-13
 
907
Feb-13
 
869
Other values (96)
34643 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters237876
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJan-15
2nd rowApr-13
3rd rowJun-14
4th rowJan-15
5th rowMay-16
ValueCountFrequency (%)
May-161256
 
3.2%
Mar-131026
 
2.6%
Dec-14945
 
2.4%
May-13907
 
2.3%
Feb-13869
 
2.2%
Apr-13851
 
2.1%
Mar-12844
 
2.1%
Aug-14832
 
2.1%
Jan-14832
 
2.1%
Aug-12832
 
2.1%
Other values (91)30452
76.7%
2021-04-14T11:33:12.268203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may-161256
 
3.2%
mar-131026
 
2.6%
dec-14945
 
2.4%
may-13907
 
2.3%
feb-13869
 
2.2%
apr-13851
 
2.1%
mar-12844
 
2.1%
aug-12832
 
2.1%
jan-14832
 
2.1%
aug-14832
 
2.1%
Other values (91)30452
76.8%

Most occurring characters

ValueCountFrequency (%)
143946
18.5%
-39646
16.7%
a11087
 
4.7%
e9738
 
4.1%
39458
 
4.0%
u9401
 
4.0%
49269
 
3.9%
J9200
 
3.9%
28904
 
3.7%
M8046
 
3.4%
Other values (22)79181
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79292
33.3%
Decimal Number79292
33.3%
Uppercase Letter39646
16.7%
Dash Punctuation39646
16.7%

Most frequent character per category

ValueCountFrequency (%)
a11087
14.0%
e9738
12.3%
u9401
11.9%
r6965
8.8%
c6783
8.6%
p6219
7.8%
n5974
7.5%
y4285
 
5.4%
t3271
 
4.1%
g3242
 
4.1%
Other values (4)12327
15.5%
ValueCountFrequency (%)
143946
55.4%
39458
 
11.9%
49269
 
11.7%
28904
 
11.2%
02544
 
3.2%
52431
 
3.1%
62044
 
2.6%
9559
 
0.7%
8137
 
0.2%
ValueCountFrequency (%)
J9200
23.2%
M8046
20.3%
A6446
16.3%
D3512
 
8.9%
O3271
 
8.3%
F3211
 
8.1%
S3015
 
7.6%
N2945
 
7.4%
ValueCountFrequency (%)
-39646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin118938
50.0%
Common118938
50.0%

Most frequent character per script

ValueCountFrequency (%)
a11087
 
9.3%
e9738
 
8.2%
u9401
 
7.9%
J9200
 
7.7%
M8046
 
6.8%
r6965
 
5.9%
c6783
 
5.7%
A6446
 
5.4%
p6219
 
5.2%
n5974
 
5.0%
Other values (12)39079
32.9%
ValueCountFrequency (%)
143946
36.9%
-39646
33.3%
39458
 
8.0%
49269
 
7.8%
28904
 
7.5%
02544
 
2.1%
52431
 
2.0%
62044
 
1.7%
9559
 
0.5%
8137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII237876
100.0%

Most frequent character per block

ValueCountFrequency (%)
143946
18.5%
-39646
16.7%
a11087
 
4.7%
e9738
 
4.1%
39458
 
4.0%
u9401
 
4.0%
49269
 
3.9%
J9200
 
3.9%
28904
 
3.7%
M8046
 
3.4%
Other values (22)79181
33.3%

last_pymnt_amnt
Real number (ℝ≥0)

Distinct34930
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2678.826162
Minimum0
Maximum36115.2
Zeros74
Zeros (%)0.2%
Memory size310.4 KiB
2021-04-14T11:33:12.480510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.34
Q1218.68
median546.14
Q33293.16
95-th percentile12183.944
Maximum36115.2
Range36115.2
Interquartile range (IQR)3074.48

Descriptive statistics

Standard deviation4447.136012
Coefficient of variation (CV)1.660106234
Kurtosis8.867819694
Mean2678.826162
Median Absolute Deviation (MAD)449.45
Skewness2.712122241
Sum106394938.7
Variance19777018.71
MonotocityNot monotonic
2021-04-14T11:33:12.670092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074
 
0.2%
276.0621
 
0.1%
20017
 
< 0.1%
5016
 
< 0.1%
10015
 
< 0.1%
40012
 
< 0.1%
773.4412
 
< 0.1%
15011
 
< 0.1%
786.0111
 
< 0.1%
50011
 
< 0.1%
Other values (34920)39517
99.5%
ValueCountFrequency (%)
074
0.2%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.131
 
< 0.1%
ValueCountFrequency (%)
36115.21
< 0.1%
35613.681
< 0.1%
35596.411
< 0.1%
35479.891
< 0.1%
35471.861
< 0.1%

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct106
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size310.4 KiB
May-16
10308 
Apr-16
2547 
Mar-16
 
1123
Feb-13
 
843
Feb-16
 
736
Other values (101)
24158 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238290
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowMay-16
2nd rowSep-13
3rd rowMay-16
4th rowApr-16
5th rowMay-16
ValueCountFrequency (%)
May-1610308
26.0%
Apr-162547
 
6.4%
Mar-161123
 
2.8%
Feb-13843
 
2.1%
Feb-16736
 
1.9%
Jan-16657
 
1.7%
Dec-15647
 
1.6%
Mar-13577
 
1.5%
Mar-14564
 
1.4%
Dec-14562
 
1.4%
Other values (96)21151
53.3%
2021-04-14T11:33:13.081155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may-1610308
26.0%
apr-162547
 
6.4%
mar-161123
 
2.8%
feb-13843
 
2.1%
feb-16736
 
1.9%
jan-16657
 
1.7%
dec-15647
 
1.6%
mar-13577
 
1.5%
mar-14564
 
1.4%
dec-14562
 
1.4%
Other values (96)21151
53.3%

Most occurring characters

ValueCountFrequency (%)
141601
17.5%
-39715
16.7%
a17601
 
7.4%
M15523
 
6.5%
615371
 
6.5%
y12231
 
5.1%
r7664
 
3.2%
e7600
 
3.2%
p6483
 
2.7%
A6411
 
2.7%
Other values (23)68090
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79430
33.3%
Decimal Number79430
33.3%
Uppercase Letter39715
16.7%
Dash Punctuation39715
16.7%

Most frequent character per category

ValueCountFrequency (%)
a17601
22.2%
y12231
15.4%
r7664
9.6%
e7600
9.6%
p6483
 
8.2%
u5856
 
7.4%
c4475
 
5.6%
n3834
 
4.8%
b3075
 
3.9%
o2225
 
2.8%
Other values (4)8386
10.6%
ValueCountFrequency (%)
141601
52.4%
615371
 
19.4%
46255
 
7.9%
55502
 
6.9%
35164
 
6.5%
24079
 
5.1%
01153
 
1.5%
9228
 
0.3%
841
 
0.1%
736
 
< 0.1%
ValueCountFrequency (%)
M15523
39.1%
A6411
16.1%
J5895
 
14.8%
F3075
 
7.7%
D2414
 
6.1%
N2225
 
5.6%
S2111
 
5.3%
O2061
 
5.2%
ValueCountFrequency (%)
-39715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119145
50.0%
Common119145
50.0%

Most frequent character per script

ValueCountFrequency (%)
a17601
14.8%
M15523
13.0%
y12231
10.3%
r7664
 
6.4%
e7600
 
6.4%
p6483
 
5.4%
A6411
 
5.4%
J5895
 
4.9%
u5856
 
4.9%
c4475
 
3.8%
Other values (12)29406
24.7%
ValueCountFrequency (%)
141601
34.9%
-39715
33.3%
615371
 
12.9%
46255
 
5.2%
55502
 
4.6%
35164
 
4.3%
24079
 
3.4%
01153
 
1.0%
9228
 
0.2%
841
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII238290
100.0%

Most frequent character per block

ValueCountFrequency (%)
141601
17.5%
-39715
16.7%
a17601
 
7.4%
M15523
 
6.5%
615371
 
6.5%
y12231
 
5.1%
r7664
 
3.2%
e7600
 
3.2%
p6483
 
2.7%
A6411
 
2.7%
Other values (23)68090
28.6%

pub_rec_bankruptcies
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing697
Missing (%)1.8%
Memory size310.4 KiB
0.0
37339 
1.0
 
1674
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters117060
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.037339
94.0%
1.01674
 
4.2%
2.07
 
< 0.1%
(Missing)697
 
1.8%
2021-04-14T11:33:13.429971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T11:33:13.535046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.037339
95.7%
1.01674
 
4.3%
2.07
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
076359
65.2%
.39020
33.3%
11674
 
1.4%
27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78040
66.7%
Other Punctuation39020
33.3%

Most frequent character per category

ValueCountFrequency (%)
076359
97.8%
11674
 
2.1%
27
 
< 0.1%
ValueCountFrequency (%)
.39020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common117060
100.0%

Most frequent character per script

ValueCountFrequency (%)
076359
65.2%
.39020
33.3%
11674
 
1.4%
27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII117060
100.0%

Most frequent character per block

ValueCountFrequency (%)
076359
65.2%
.39020
33.3%
11674
 
1.4%
27
 
< 0.1%

Interactions

2021-04-14T11:31:05.983085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:31:06.343322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:31:06.537910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:31:06.719019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T11:32:34.501579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:34.693895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:34.927926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:35.155535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:35.382179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:35.612291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:35.812500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:35.981607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:36.180328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:36.388082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:36.589485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:36.824389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:37.005626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:37.174310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:37.355836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:37.537434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:37.722121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:37.910650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:38.100335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:38.290498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:38.473295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:38.662740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:38.846341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:39.025881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:39.214808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:39.390439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:39.570551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:39.769007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:39.948499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:40.222767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:40.422326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:40.609782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:40.871994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:41.073594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:41.248305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:41.467569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:41.714603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:41.975357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:42.176079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:42.371342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:42.706260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:42.991509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:43.184228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:43.388588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:43.591401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:43.841872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:44.092874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:44.313209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:44.515056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:44.690899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:44.879908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:45.088683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:45.279557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:45.465808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:45.653713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:45.827891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:46.015672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T11:32:46.198303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-14T11:33:13.694214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T11:33:14.145167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T11:33:14.582234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T11:33:15.044583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-14T11:33:15.530308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-14T11:32:47.173978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-14T11:32:51.183247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-14T11:32:52.210937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-14T11:32:52.621647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

loan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposetitleaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqopen_accpub_recrevol_balrevol_utiltotal_accout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dpub_rec_bankruptcies
0500050004,975.0036 months10.65%162.87BB2NaN10+ yearsRENT24,000.00VerifiedDec-11Fully Paidcredit_cardComputerAZ27.650Jan-851NaN301364883.70%90.000.005,863.165,833.845,000.00863.160.000.000.00Jan-15171.62May-160.00
1250025002,500.0060 months15.27%59.83CC4Ryder< 1 yearRENT30,000.00Source VerifiedDec-11Charged OffcarbikeGA1.000Apr-995NaN3016879.40%40.000.001,008.711,008.71456.46435.170.00117.081.11Apr-13119.66Sep-130.00
2240024002,400.0036 months15.96%84.33CC5NaN10+ yearsRENT12,252.00Not VerifiedDec-11Fully Paidsmall_businessreal estate businessIL8.720Nov-012NaN20295698.50%100.000.003,005.673,005.672,400.00605.670.000.000.00Jun-14649.91May-160.00
3100001000010,000.0036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49,200.00Source VerifiedDec-11Fully PaidotherpersonelCA20.000Feb-96135.00100559821%370.000.0012,231.8912,231.8910,000.002,214.9216.970.000.00Jan-15357.48Apr-160.00
4300030003,000.0060 months12.69%67.79BB5University Medical Group1 yearRENT80,000.00Source VerifiedDec-11CurrentotherPersonalOR17.940Jan-96038.001502778353.90%38524.06524.063,513.333,513.332,475.941,037.390.000.000.00May-1667.79May-160.00
5500050005,000.0036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36,000.00Source VerifiedDec-11Fully PaidweddingMy wedding loan I promise to pay backAZ11.200Nov-043NaN90796328.30%120.000.005,632.215,632.215,000.00632.210.000.000.00Jan-15161.03Jan-160.00
6700070007,000.0060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47,004.00Not VerifiedDec-11Fully Paiddebt_consolidationLoanNC23.510Jul-051NaN701772685.60%110.000.0010,110.8410,110.846,985.613,125.230.000.000.00May-161,313.76May-160.00
7300030003,000.0036 months18.64%109.43EE1MKC Accounting9 yearsRENT48,000.00Source VerifiedDec-11Fully PaidcarCar DownpaymentCA5.350Jan-072NaN40822187.50%40.000.003,939.143,939.143,000.00939.140.000.000.00Jan-15111.34Dec-140.00
8560056005,600.0060 months21.28%152.39FF2NaN4 yearsOWN40,000.00Source VerifiedDec-11Charged Offsmall_businessExpand Business & Buy Debt PortfolioCA5.550Apr-042NaN110521032.60%130.000.00646.02646.02162.02294.940.00189.062.09Apr-12152.39Aug-120.00
9537553755,350.0060 months12.69%121.45BB5Starbucks< 1 yearRENT15,000.00VerifiedDec-11Charged OffotherBuilding my credit history.TX18.080Sep-040NaN20927936.50%30.000.001,476.191,469.34673.48533.420.00269.292.52Nov-12121.45Mar-130.00

Last rows

loan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposetitleaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqopen_accpub_recrevol_balrevol_utiltotal_accout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dpub_rec_bankruptcies
3970750005000525.0036 months9.33%159.77BB3Stark and Roth Inc2 yearsMORTGAGE180,000.00Not VerifiedJul-07Fully Paidhome_improvementhome improvment loanWI11.930Feb-9510.001606056839.20%380.000.005,751.53603.915,000.00751.530.000.000.00Jul-10161.55Jun-07NaN
3970850005000375.0036 months9.96%161.25BB5Millenium Group4 yearsMORTGAGE48,000.00Not VerifiedJul-07Fully Paiddebt_consolidationTito5000FL8.030Aug-9510.00602832948.60%60.000.005,804.73435.365,000.00804.730.000.000.00Jul-10162.07Jun-10NaN
3970950005000675.0036 months11.22%164.23CC4Self-Employeed< 1 yearOWN80,000.00Not VerifiedJul-07Fully Paidcredit_cardP's Family Credit LoanWI1.210Jul-9630.001512718516.10%290.000.005,912.05798.135,000.00912.050.000.000.00Jul-10165.17Jun-07NaN
3971050005000250.0036 months7.43%155.38AA2Rush Univ Med Grp1 yearOWN85,000.00Not VerifiedJul-07Fully Paidcredit_cardMy Credit Card LoanWI0.310Oct-9700.00702160.60%190.000.005,593.63279.685,000.00593.630.000.000.00Jul-10156.29Jun-07NaN
3971150005000700.0036 months8.70%158.30BB1A. F. Wolfers, Inc.5 yearsMORTGAGE75,000.00Not VerifiedJul-07Fully Paidcredit_cardReduce Credit Card DebtCO15.550May-9400.001006603323%290.000.005,698.60797.805,000.00698.600.000.000.00Jul-10159.83Nov-14NaN
39712250025001,075.0036 months8.07%78.42AA4FiSite Research4 yearsMORTGAGE110,000.00Not VerifiedJul-07Fully Paidhome_improvementHome ImprovementCO11.330Nov-9000.00130727413.10%400.000.002,822.971,213.882,500.00322.970.000.000.00Jul-1080.90Jun-10NaN
3971385008500875.0036 months10.28%275.38CC1Squarewave Solutions, Ltd.3 yearsRENT18,000.00Not VerifiedJul-07Fully Paidcredit_cardRetiring credit card debtNC6.401Dec-8615.0060884726.90%90.000.009,913.491,020.518,500.001,413.490.000.000.00Jul-10281.94Jul-10NaN
39714500050001,325.0036 months8.07%156.84AA4NaN< 1 yearMORTGAGE100,000.00Not VerifiedJul-07Fully Paiddebt_consolidationMBA Loan ConsolidationMA2.300Oct-9800.00110969819.40%200.000.005,272.161,397.125,000.00272.160.000.000.00Apr-080.00Jun-07NaN
3971550005000650.0036 months7.43%155.38AA2NaN< 1 yearMORTGAGE200,000.00Not VerifiedJul-07Fully PaidotherJAL LoanMD3.720Nov-8800.00170856070.70%260.000.005,174.20672.665,000.00174.200.000.000.00Jan-080.00Jun-07NaN
3971675007500800.0036 months13.75%255.43EE2Evergreen Center< 1 yearOWN22,000.00Not VerifiedJun-07Fully Paiddebt_consolidationConsolidation LoanMA14.291Oct-03011.0070417551.50%80.000.009,195.26980.837,500.001,695.260.000.000.00Jun-10256.59Jun-10NaN